One of factors required to allow an automobile to run safely is a tire air pressure. When the air pressure is lower than an appropriate value, the stable maneuverability or fuel consumption is deteriorated, which may cause a tire burst. Thus, Tire Pressure Monitoring System (TPMS) for detecting a tire having a decreased pressure to send an alarm to the driver to prompt an appropriate action is an important technique from the viewpoint of environment protection and driver safety.
A conventional alarm apparatus can be classified into two types of the direct detection-type one (direct TPMS) and the indirect detection-type one (indirect TPMS). The direct TPMS provides a pressure sensor in a tire wheel to thereby directly measure the tire pressure. The direct TPMS can detect a decrease in the pressure at a high accuracy but requires exclusive wheels and has a problematic fault-tolerance performance in an actual environment for example. Thus, the direct TPMS is still disadvantageous in the technical and cost aspects.
On the other hand, the indirect TPMS is a method of estimating the air pressure based on the tire rotation information. The indirect TPMS can be further classified into the Dynamic Loaded Radius (DLR) method and the Resonance Frequency Mechanism (RFM) method. The DLR method is a method that uses a phenomenon according to which a tire having a decreased pressure in a running vehicle is collapsed and thus the tire has a reduced dynamic loaded radius and is consequently rotated at a higher speed than other tires having a normal pressure. The DLR method compares the rotation speeds of the four tires to thereby detect a tire having a decreased pressure. Since this method can use only wheel rotation speed signals obtained from a wheel speed sensor to subject the signals to a relatively-easy computation processing, this method has been widely researched mainly for the purpose of detecting a puncture of one wheel. However, this method merely makes a relative comparison among wheel rotation speeds and thus cannot detect a case of four wheels simultaneous deflation (natural leakage). Furthermore, a disadvantage is caused where a decreased pressure cannot be accurately detected through all running conditions because a difference in the wheel speed is also caused by running conditions such as the turning of the vehicle, the acceleration and deceleration, and an eccentric load.
On the other hand, the RFM method is a method to use a fact that a tire having a decreased pressure has a different wheel speed signal frequency characteristic to thereby detect a difference from a normal pressure. In contrast with the DLR method, the RFM method is an absolute comparison with the normal values of the respective wheels that are retained in advance. Thus, the RFM method also can detect a case of four wheels simultaneous deflation. Thus, the RFM method attracts attentions as a better indirect detection method. However, the RFM method has a disadvantage where some running conditions cause strong noise for example and thus an estimated frequency value of a target domain is not robust against the vehicle speed and the road surface situation for example. The present invention relates to an apparatus for detecting a tire status based on the RFM method. Hereinafter, the basic principle of this method will be described in more detail.
When the vehicle is running, the tires receive a force from the road surface to thereby cause the torsional motion in the front-and-rear direction and the front-and-rear motion of the suspension, and these motions have a coupled resonance vibration. Since this resonance phenomenon also has an influence on the wheel rotation motion, a wheel speed signal obtained from a wheel sensor provided in the Anti-Lock Braking System (ABS) also includes information related to the resonance phenomenon. Furthermore, since the coupled resonance vibration is caused in a unique vibration mode due to the tire torsional rigidity, the excitation status thereof changes so as to depend only on a change in the air pressure constituting the tire physical characteristic and has a very small dependence on a change in the vehicle speed and a change in the road surface. Specifically, a decreased air pressure causes a change in the dynamics of the tire torsional motion. Thus, when the wheel speed signal is subjected to a frequency analysis, a peak of the coupled resonance vibration (resonance peak) appears at the lower frequency-side in the case of a decreased pressure than in the case of a normal pressure.
FIG. 3 illustrates the power spectra obtained by attaching tires having a normal air pressure, tires having a 25%-decreased pressure (200 kPa) from the normal pressure (270 kPa), and tires having a 40%-decreased pressure (160 kPa) to a vehicle and subjecting the respective wheel acceleration signals obtained within a fixed time (2 minutes) (which are obtained by calculating a time difference of wheel speed signals) to the Fast Fourier Transform (FFT). In FIG. 3, the horizontal axis shows frequency (Hz) and the vertical axis shows decibel (dB). The data used was obtained by allowing the vehicle to run on a road having a markedly-uneven surface with a speed of 40 km per hour. The components in the vicinity of 20 to 40 Hz show the vibration caused by the resonance between the tire vibration in the front-and-rear direction and the vehicle suspension. It is understood that a change in the internal pressure causes a frequency having a peak value (resonance frequency) to move to a lower frequency. This phenomenon appears, due to the above-described characteristic, to be independent from the tire type and the vehicle type, the running speed, and the road surface situation for example. Thus, the RFM method focuses on this resonance frequency and issues an alarm when the frequency is relatively lower than a reference frequency estimated during initialization. Thus, the resonance frequency must be estimated based on wheel speed signals obtained from the ABS. However, since it is difficult to store time-series data in an in-vehicle calculator having a limited calculation resource, a difficulty is caused in performing the frequency analysis based on FFT. Due to this reason, the conventional method is to describe a resonance phenomenon by a quadratic model to perform a sequential time-series analysis based on an Autoregressive (AR) model (see Patent Literature 1 for example). A frequency corresponding to the pole of a transfer function representing a AR model is estimated as a resonance frequency. Thus, a resonance frequency can be accurately obtained if the resonance peak is correctly extracted by the model.
A wheel speed signal, which is an input of the Tire Pressure Monitoring System based on the RFM method, is calculated based on a time signal called “time stamp” supplied from the ABS. Here, the ABS obtains the time stamp information in the manner as described below. A part at which the vehicle is connected to a tire has a gear associated with the tire. An in-vehicle wheel sensor measures, based on an induction voltage, a change in the magnetic field emitted from a permanent magnet stored therein due to gear rotation. By converting this voltage change to a rectangular wave, the time at which the rising edges of the respective teeth of the gear pass can be measured (see FIG. 4). This passing time is a time stamp based on which the tire wheel speed is calculated.
Specifically, by calculating a difference between the time stamps, the time required for one tooth to pass (hereinafter, this passing time of one tooth is called “time information”) can be calculated. Thus, based on this time information and the interval between neighboring teeth (which can be calculated based on a known gear radius), the speed at which the tooth momentarily passes can be calculated. The wheel speed signal obtained by this method can be obtained by a dynamic cycle depending on the vehicle running speed (hereinafter, the speed calculated in the manner as described above will be called “dynamic wheel speed signal”). This is not appropriate for a data format for a digital signal processing. The reason is that currently-existing digital signal processing techniques are based on an assumption that data is obtained with a fixed cycle under a steady environment. Thus, if this assumption cannot be satisfied due to a case in which the sampling cycle changes in accordance with the vehicle running speed for example, the frequency characteristic cannot be acquired correctly. Due to this reason, the data sampling technique must be improved so that a wheel speed signal can be always obtained at a fixed cycle. At the same time, in a process of calculating (since this process resamples the static wheel speed signal based on once-sampled time information, this conversion process will be called “resampling” hereinafter), from the information obtained at a dynamic cycle, a wheel speed signal having a fixed cycle (hereinafter this will be called “static wheel speed signal” and, when the term “wheel speed signal” or “wheel speed” is simply used hereinafter, the term denotes the “static wheel speed signal”), the wheel speed is desirably calculated correctly by eliminating the influence by noise as much as possible.
By the way, the method of resampling a wheel speed signal can be classified, on the basic principles thereof, into the following two types of: (1) a method of using interpolation to directly calculate a static wheel speed signal; and (2) a method of calculating the number of teeth passed per a sampling cycle to thereby calculate a static wheel speed signal.
Among these methods, the method (1) calculates dynamic wheel speed signals based on the time information to calculate a straight line around which those signals are temporally close to one another to thereby calculate a static wheel speed signal corresponding to the sampling time. Non Patent Literature 1 suggests a method to carry out a linear interpolation using two dynamic wheel speed signals to thereby resample static wheel speed signals (see FIG. 5). In FIG. 5, the dotted line shows each time of 5 ms at which a static wheel speed signal is cut out. The circle mark shows a dynamic wheel speed signal. The diamond mark shows a static wheel speed signal. It has been pointed out that the method of Non Patent Literature 1 has the following two disadvantages.
Firstly, when a cycle at which a dynamic wheel speed signal is obtained is, due to a low-speed running, longer than a sampling cycle, a plurality of wheel speed signals are extracted through a single interpolation. However, these wheel speed signals have thereamong only a simple linear relation, thus losing the tire vibration-related information originally included in the time information.
Secondly, dynamic wheel speed signals used as data for resampling are merely two neighboring points. This means that some obtained information is not used, causing a poor resampling accuracy. Non Patent Literature 1 suggests, as a method that can solve the disadvantage as described above, a method using a low-pass filter and the decimation of a resampled wheel speed signal. However, this method does not provide a fundamental solution to all conditions and also requires a large amount of calculation.
Therefore, this method is not practical. Thus, a method may be considered where this resampling method by the linear interpolation is amplified to increase the number of data points used for the interpolation by the regression line thereof.
In the case of this method however, as the speed is lower, the overlap is caused between the data used for the regression in order to calculate a wheel speed signal at a certain time and the data used for the regression in order to calculate a wheel speed signal at the next time. The regression by the data overlapped in the front-and-rear direction as described above is equivalent to subjecting the time-series data to a moving average processing and requires a low-pass filter having an effect depending on the vehicle speed. Thus, a third disadvantage is newly caused. Particularly under the running conditions having a speed of 40 kilometers or less per hour, the filter effect is strong and a high influence is also caused on the frequency band to which attention is paid for detecting an abnormality, which is not desirable. Furthermore, Non Patent Literature 1 discloses, for example, a method of using a non-linear kernel and a method of using an approximated analog filter. However, any of these methods lacks in the physical basis and also provides a small effect to the required calculation amount and thus is insufficient as a method.
On the other hand, the method (2) calculates, based on the acquired time information, the number of teeth passed per a unit time (sampling cycle set in advance) to calculate the wheel speed based on the relation between the distance (a product of the interval between neighboring teeth and the number of passed teeth) and the time (sampling cycle). This method has been frequently suggested and carried out as the straightest approach. For example, in the case of a Tire Pressure Monitoring System based on the DLR method, in order to calculate the wheel speed signal per 40 milliseconds, the accumulated value of the time information is sequentially calculated and the number of teeth at which the accumulated value is closest to 40 milliseconds is adopted as a distance to the accumulated time. However, in the RFM method, such a simple calculation method cannot be applied due to the two reasons as described below.
Firstly, in the RFM method for monitoring the frequency characteristic close to 40 Hz, the wheel speed signal is desirably obtained with the shortest cycle as possible. Thus, the long sampling cycle of 40 milliseconds is far from satisfying the required performance. Specifically, a sampling cycle of at least 8 milliseconds or shorter is required. Furthermore, when considering the limitation on the ABS calculation resource, the calculation is desirably achieved with the lightest processing as possible.
Secondly, a sufficient accuracy required to acquire the frequency characteristic cannot be secured. Specifically, the number of teeth counted is always an integer and thus the wheel speed is calculated based on the accumulated time of about 40 milliseconds (thus, the sampling cycle is not strictly fixed and thus this method is a method in the intermediate of a method of simply calculating a dynamic wheel speed signal and a method of accurately performing resampling). Thus, in the case of the calculation including such a rough part, the data accuracy is remarkably insufficient in the RFM method that must detect a minute change in the resonance frequency occurring on the order of a few Hz. Furthermore, an influence by an error due to the approximate calculation of the accumulated time increases as the sampling cycle is shorter. In order to solve the first disadvantage, it is not effective to simply reduce the sampling cycle. In order to also solve the second disadvantage, a short cycle must be fixed and the number of teeth passed during the cycle must be calculated strictly. Furthermore, an improvement for reducing white sensing noise is also required.
The resampling method as described above has been recognized as being difficult for application because of the high calculation load. Thus, no detailed examination or development has been performed for the resampling method. Actually, Patent Literature 2 avoids this method due to the high calculation amount and suggests an air pressure alarm apparatus based on another approach.